量子力学基态能量计算的改进蚁群优化算法

Improved Ant Colony Optimization Algorithms for Ground State Energy of Quantum Mechanical Systems

  • 摘要: 为进一步减少迭代次数和改善解的质量,对蚁群优化方法进行改进.在求解体系基态能上与传统的变分法相比有很大的优势.求解了氦原子基态能量,并应用于不同半径量子点中砷化镓类氢施主基态能量的计算.通过与变分法和遗传算法的比较,展示了算法的性能.

     

    Abstract: Ant colony optimization(ACO),a global optimization method,is proposed to analyze ground state energy of quantum mechanical systems.It simulates the way that real ants find a shortest path from nest to food source and back.In order to reduce iterations and improve solutions,ACO algorithm for ground state energy is modified.The proposed method exhibits advantage compared with traditional variation method.Ground state energy of helium atom and hydrogenic donors in GaAs-(Ga,Al)As quantum dot are calculated.The algorithm is demonstrated via comparison with variational method and genetic algorithms (GAs).

     

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